Search Results for author:
Found 3 papers, 0 papers with code
Nothing makes sense in deep learning, except in the light of evolution
These deconstraints can be very helpful to both the particular algorithm in how it handles challenges in implementation and the overall field of DL in how easy it is for new ideas to be generated.
Steepest ascent can be exponential in bounded treewidth problems
We investigate the complexity of local search based on steepest ascent.
Complexity of evolutionary equilibria in static fitness landscapes
I do this by showing that the popular NK model of rugged fitness landscapes is PLS-complete for K >= 2; the reduction from Weighted 2SAT is a bijection on adaptive walks, so there are NK fitness landscapes where every adaptive path from some vertices is of exponential length.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
